Selecting green cover species in the under-trellis zone of Lower Austrian vineyards

One of the most important effects of climate change in wine-growing areas is the advance of phenological stages, especially concerning early berry ripening. In the hottest seasons, this results in a lack of synchrony between sugar and phenolic ripeness. In order to cope with this fact, a general effort is being made by researchers and growers aiming at delaying ripening through different strategies. One of the proposed approaches is the application of elicitors. This study aims to assess the effect at the transcriptomic level of application of three elicitors (Vitalfit, Fruitel, and Protone) in Tempranillo.

The application of remote and proximal sensing is a fast and efficient method to monitor grapevine vegetative and physiological parameters and is considered valuable to derive information on associated yield and quality traits in the vineyard. Further details can be obtained by the application of molecular analysis at the gene expression level aiming at elucidating how pathways controlling the formation of different grape quality traits are influenced by spatial variability. This work aims at evaluating intra-vineyard variability in grape composition at harvest and at comparing this with remotely sensed canopy vegetation data and molecular-based approaches.

Two of the most harmful microbial metabolites for human health that can be present in wines and either fermented or raw foods are biogenic amines (BA) and ochratoxine A (OTA). Winemakers are aware of the need to avoid their presence in wine by using different strategies, one of them is the use of enzymes. Some recombinant laccases have been characterized and revealed as potential tools to degrade these toxic compounds in wine[1], specifically biogenic amines[2].

Grapevine cultivars are vegetatively propagated to preserve their varietal characteristics. However, spontaneous somatic variations that occur and are maintained during cycles of vegetative growth offer opportunities for the natural improvement of traditional grape cultivars. One advantageous trait for winegrowing is reduced bunch compactness, which decreases the susceptibility to pests and fungal diseases and favor an even berry ripening.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).